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Ordinary Least Squares Regression
2021This chapter provides an introduction to ordinary least squares (OLS) regression analysis in R. This is a technique used to explore whether one or multiple variables (the independent variable or X) can predict or explain the variation in another variable (the dependent variable or Y).
Alese Wooditch +4 more
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A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands
Social Science Research Network, 2018It is standard practice in applied work to study the effect of a binary variable ("treatment") on an outcome of interest using linear models with additive effects.
Tymon Sloczy'nski
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Weighted Least Squares Fitting Using Ordinary Least Squares Algorithms
Psychometrika, 1997A general approach for fitting a model to a data matrix by weighted least squares (WLS) is studied. This approach consists of iteratively performing (steps of) existing algorithms for ordinary least squares (OLS) fitting of the same model. The approach is based on minimizing a function that majorizes the WLS loss function.
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The lower tail of random quadratic forms with applications to ordinary least squares
arXiv.org, 2013Finite sample properties of random covariance-type matrices have been the subject of much research. In this paper we focus on the “lower tail” of such a matrix, and prove that it is sub-Gaussian under a simple fourth moment assumption on the one ...
R. Oliveira
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Feasible generalized least squares for panel data with cross-sectional and serial correlations
, 2019This paper considers generalized least squares (GLS) estimation for linear panel data models. By estimating the large error covariance matrix consistently, the proposed feasible GLS estimator is more efficient than the ordinary least squares in the ...
Jushan Bai, Sungchun Choi, Yuan Liao
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A risk comparison of ordinary least squares vs ridge regression
Journal of machine learning research, 2011We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a principal component analysis) and then performs an ordinary (un-regularized)
Paramveer S. Dhillon +3 more
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